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Joint Regression Modeling and Sparse Spatial Refinement for High-Quality Reconstruction of Distorted Color Images

Abstract: 

High quality algorithms are demanded to reconstruct distorted color images in a variety of applications. For example, distortions can result during transmission over lossy channels in image coding or in multi-view imaging scenarios. In general, not all color channels are equally affected and the losses distribute differently in-between channels. However, state-of-the-art methods process color channels independently and do not take the cross color information into account. Thus, a novel and powerful reconstruction algorithm is formulated in this contribution that exploits color as well as spatial information. Therefore, an initial model is estimated for the distorted area using a reference channel. Then, its quality is estimated and a spatial weighting model is set-up. Afterwards, the initial inter channel prediction is refined by generating a sparse model that takes the spatial correlations into account, as well. Consequently, the proposed method achieves an outstanding quality compared to state-of-the-art methods.

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Paper Details

Authors:
Jürgen Seiler, André Kaup
Submitted On:
19 September 2019 - 8:34am
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Nils Genser
Paper Code:
1253
Document Year:
2019
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Document Files

presentation_genser_icip2019.pptx

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[1] Jürgen Seiler, André Kaup, "Joint Regression Modeling and Sparse Spatial Refinement for High-Quality Reconstruction of Distorted Color Images", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4729. Accessed: Mar. 30, 2020.
@article{4729-19,
url = {http://sigport.org/4729},
author = {Jürgen Seiler; André Kaup },
publisher = {IEEE SigPort},
title = {Joint Regression Modeling and Sparse Spatial Refinement for High-Quality Reconstruction of Distorted Color Images},
year = {2019} }
TY - EJOUR
T1 - Joint Regression Modeling and Sparse Spatial Refinement for High-Quality Reconstruction of Distorted Color Images
AU - Jürgen Seiler; André Kaup
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4729
ER -
Jürgen Seiler, André Kaup. (2019). Joint Regression Modeling and Sparse Spatial Refinement for High-Quality Reconstruction of Distorted Color Images. IEEE SigPort. http://sigport.org/4729
Jürgen Seiler, André Kaup, 2019. Joint Regression Modeling and Sparse Spatial Refinement for High-Quality Reconstruction of Distorted Color Images. Available at: http://sigport.org/4729.
Jürgen Seiler, André Kaup. (2019). "Joint Regression Modeling and Sparse Spatial Refinement for High-Quality Reconstruction of Distorted Color Images." Web.
1. Jürgen Seiler, André Kaup. Joint Regression Modeling and Sparse Spatial Refinement for High-Quality Reconstruction of Distorted Color Images [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4729